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This page documents the internal runtime behavior of the ai step when executed by Codemod’s Rig-based harness.
The workflow contract does not change. Keep using the same ai.* fields documented in Workflow Reference.

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What the harness provides

  • Rig-based agent loop for ai steps.
  • Multi-provider execution with protocol-specific clients.
  • Built-in tool server wiring for Codemod CLI tools.
  • Parent coding-agent handoff mode.
  • Context compaction with bounded retries.
  • Structured logs for long-running steps and compaction events.

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Supported LLM protocols

The harness supports:
  • openai
  • anthropic
  • google_ai
  • azure_openai
Unknown protocol values fail fast.

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Built-in tools

Default enabled tools:
  • bash
  • str_replace_based_edit_tool
  • glob
  • sequentialthinking
  • task_done
  • json_edit_tool
  • ckg_tool
Available but not default-enabled:
  • mcp_tool

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Parent-agent handoff mode

Before running Rig, Codemod checks whether it is executing under a known coding-agent parent context. Recognized agent families include:
  • codex
  • claude-code
  • aider
  • cursor
  • windsurf
  • goose
  • opencode
  • openclaw
If detection confidence is detected, the step emits:
  • [AI INSTRUCTIONS]
  • prompt/system content
  • [/AI INSTRUCTIONS]
and skips Rig execution for that step. If confidence is uncertain or not_detected, normal Rig execution continues.

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Memory and context management

The harness controls context growth with a hybrid strategy:
  1. Proactive guard:
  • Estimates prompt + history size before each completion turn.
  • Triggers compaction when the soft budget is exceeded.
  1. Reactive guard:
  • Detects provider context-limit style failures.
  • Triggers compaction and retries.
  1. Compaction pipeline:
  • Deterministic pruning of older turns while preserving anchors/recent history.
  • Hierarchical summarization of archived context.
  • Memory packet rebuild of history.
  1. Bounded retries:
  • Maximum compaction attempts: 5.
  • If still oversized, returns explicit memory exhaustion error.

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Semantic retrieval behavior

When embeddings are available for the selected provider path, the harness builds an in-memory vector index and injects retrieved dynamic context into agent calls.
  • Enabled path: openai, google_ai, azure_openai.
  • Fallback path: anthropic runs without dynamic vector context injection.

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Token behavior

The harness does not force an explicit max_tokens request. Output token limits are left to provider/model defaults. Context-window handling is managed separately through compaction and bounded retry behavior.

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Observability

Engine logs include:
  • handoff detection decision (handoff vs rig)
  • periodic progress (AI step still running ...)
  • compaction events (AI memory compaction applied ...)
  • explicit memory exhaustion diagnostics when retries are exhausted